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Cursor Chat History Vectorizer & Dockerized Search MCP Server

@markelaugust74

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stdio

作者

markelaugust74

Cursor Chat History Vectorizer & Dockerized Search MCP Server について

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

#api#docker#self-hosted#embeddings#chat-history#rag#fastapi#vector-database#local-llm#lancedb#ollama#mcp-server

Cursor Chat History Vectorizer & Dockerized Search MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "cursor-history-mcp-markelaugust74": {
      "command": "python",
      "args": [
        "cursor_history_extractor.py"
      ]
    }
  }
}

Cursor Chat History Vectorizer & Dockerized Search MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Cursor Chat History Vectorizer & Dockerized Search MCP?

This server extracts user prompts from Cursor IDE’s local SQLite databases, generates embeddings via a local Ollama instance (using nomic-embed-text), stores them in a LanceDB vector database, and provides a Dockerized FastAPI API for vector similarity search. It is designed for developers who want to make their Cursor chat history searchable for Retrieval Augmented Generation (RAG) or LLM-based analysis.

How to use Cursor Chat History Vectorizer & Dockerized Search MCP?

Two main steps: (1) Run python cursor_history_extractor.py on your host machine to create or update the LanceDB database at ./cursor_chat_history.lancedb. (2) Build the Docker image with docker build -t cursor-chat-search-api . and run the container with docker run -p 8001:8001 -v /absolute/path/to/cursor_chat_history.lancedb:/data/cursor_chat_history.lancedb -e OLLAMA_HOST="http://host.docker.internal:11434" cursor-chat-search-api. The search API will be accessible at http://localhost:8001.

Key features of Cursor Chat History Vectorizer & Dockerized Search MCP

  • Extracts user prompts from Cursor’s state.vscdb files
  • Generates embeddings using local Ollama (nomic-embed-text)
  • Stores vectors and metadata in LanceDB database
  • Provides Dockerized FastAPI search server
  • Supports vector similarity search via POST /search_chat_history
  • Includes health check endpoint (GET /health)

Use cases of Cursor Chat History Vectorizer & Dockerized Search MCP

  • Search past Cursor conversations to quickly recall previous solutions
  • Feed relevant chat history into LLMs for context-aware code assistance
  • Analyze coding patterns by querying vectorized user prompts

よくある質問

1

What dependencies are required to run the extraction script?

Python 3.7+, a running Ollama instance with the `nomic-embed-text:latest` model pulled, and the Python packages `ollama`, `lancedb`, `pyarrow`, `pandas`, and `python-dotenv` (installed via `pip install -r requirements.txt`). Docker Desktop or Docker Engine is needed for the API container.

2

Where is the vector database stored?

The extraction script creates the database at `./cursor_chat_history.lancedb` on the host machine. Inside the Docker container it is expected at `/data/cursor_chat_history.lancedb` (mounted via the `-v` flag).

3

Does this tool extract AI model responses?

No. Currently only user prompts from the `aiService.prompts` key are extracted and stored. AI responses and other conversation details are not included.

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